Computational breast imaging analysis for clinical applications

Breast cancer is the most common cancer in women. Early detection has been shown to be associated with reduced breast cancer morbidity and mortality. The American Cancer Society recommends annual digital mammography screening for general population and additionally breast magnetic resonance imaging for women considered at high risk of developing breast cancer. In this talk, the speaker will talk about automated quantitative analysis of radiological breast images and employment of machine learning techniques for identifying imaging-based biomarkers for breast cancer risk assessment, intelligent diagnosis and prognosis, and treatment response evaluation.